A hybrid model using fuzzy logic and an extreme learning machine with vector particle swarm optimization for wireless sensor network localization

Phoemphon, Songyut; So-In, Chakchai; Niyato, Dusit

doi:10.1016/j.asoc.2018.01.004

Cited by 86 publications

(41 citation statements)

References 35 publications

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“…However, there are limitations to the training process where there are local minimums and large errors 33,38,39 . The PSO algorithm is derived from a simulation of bird predation behavior [40][41][42] . The fitness is used to measure the pros and cons of the particles and the optimal solution of the neural network is evaluated.…”

Section: Pso-bp Network Weightsmentioning

confidence: 99%

Photosynthetic rate prediction model of newborn leaves verified by core fluorescence parameters

Zhang

et al. 2020

Sci Rep

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Due to the imperfect development of the photosynthetic apparatus of the newborn leaves of the canopy, the photosynthesis ability is insufficient, and the photosynthesis intensity is not only related to the external environmental factors, but also significantly related to the internal mechanism characteristics of the leaves. Light suppression and even light destruction are likely to occur when there is too much external light. Therefore, focus on the newborn leaves of the canopy, the accurate construction of photosynthetic rate prediction model based on environmental factor analysis and fluorescence mechanism characteristic analysis has become a key problem to be solved in facility agriculture. According to the above problems, a photosynthetic rate prediction model of newborn leaves in canopy of cucumber was proposed. The multi-factorial experiment was designed to obtain the multi-slice large-sample data of photosynthetic and fluorescence of newborn leaves. The correlation analysis method was used to obtain the main environmental impact factors as model inputs, and core chlorophyll fluorescence parameters was used for auxiliary verification. The best modeling method PSO-BP neural network was used to construct the newborn leaf photosynthetic rate prediction model. The validation results show that the net photosynthetic rate under different environmental factors of cucumber canopy leaves can be accurately predicted. The coefficient of determination between the measured values and the predicted values of photosynthetic rate was 0.9947 and the root mean square error was 0.8787. Meanwhile, combined with the core fluorescence parameters to assist the verification, it was found that the fluorescence parameters can accurately characterize crop photosynthesis. Therefore, this study is of great significance for improving the precision of light environment regulation for new leaf of facility crops.

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Section: Pso-bp Network Weightsmentioning

confidence: 99%

Photosynthetic rate prediction model of newborn leaves verified by core fluorescence parameters

Zhang

et al. 2020

Sci Rep

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“…The results were compared with the centroid and APIT algorithms. Location error was reduced by 57% and 65% compared to the centroid and APIT algorithms, respectively [19]. Egger et al, with the help of the AOA estimation algorithm, presented a GA approach to select the relevant radar design parameters.…”

Section: Id:p0190mentioning

confidence: 99%

Localization of a Mobile Device with Sensor Using a Cascade Artificial Neural Network-Based Fingerprint Algorithm

Erdem¹,

Tuncer²,

Doğan³

2019

IJCIS

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One of the important functions of sensor networks is that they collect data from the physical environment and transmit them to a center for processing. The location from which the collected data is obtained is crucial in many applications, such as search and rescue, disaster relief, and target tracking. In this respect, determination of the location with low-cost, scalable, and efficient algorithms is required. This study presents the implementation of a fingerprint-based location determination algorithm by using the cascade artificial neural network (ANN). A 15.6 × 13.8 m 2 implementation area, in which an anchor node is placed at each corner, is divided into grids with a 60-cm edge. The proposed algorithm consists of two phases: offline and online. In the offline phase, first a mobile device with an Xbee sensor, which is able to move sensitively and communicate with anchor nodes, is used. With this device, the implementation area is visited, and at each grid point, received signal strength indicator (RSSI) values and real distances measured from the anchor nodes are recorded in a database. The training of the cascade ANN is done using the database for both range and location determination. In the online phase, the RSSIs measured by the anchor nodes are provided as the input to the cascade ANN algorithm by means of a mobile device in any coordinate. The location of the mobile device and its distance to the anchor nodes are determined with minimum error. To show the superiority of the proposed method, the results obtained are compared with those in the literature and it has been shown that this location determination is made with a smaller error.

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“…PSO algorithm performs better in terms of localization accuracy, computational complexity and/or convergence speed compared with the other optimization methods [24][25][26][27][28][29][30]. PSO and BFA (Bacterial Foraging Algorithm) are applied to localize a WSN deployed by an unmanned aerial vehicle in [24], and simulation results showed that PSO is faster than BFA.…”

Section: Related Workmentioning

confidence: 99%

“…The hybrid PSO proposed in [28] replaces positions of a half of particles with positions close to the best particle, leading to faster convergence than standard PSO. The fuzzyextreme learning machine with PSO [29] uses resultant force to move approximate node location closer to actual position. PSO-based improved DV-hop algorithm [30] uses PSO to correct estimated positions after DV-hop.…”

Section: Related Workmentioning

confidence: 99%

Localization of Large‐Scale Wireless Sensor Networks Using Niching Particle Swarm Optimization and Reliable Anchor Selection

Cui

Liang

Zhou

et al. 2018

Wireless Communications and Mobile Computing

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Due to uneven deployment of anchor nodes in large-scale wireless sensor networks, localization performance is seriously affected by two problems. The first is that some unknown nodes lack enough noncollinear neighbouring anchors to localize themselves accurately. The second is that some unknown nodes have many neighbouring anchors to bring great computing burden during localization. This paper proposes a localization algorithm which combined niching particle swarm optimization and reliable reference node selection in order to solve these problems. For the first problem, the proposed algorithm selects the most reliable neighbouring localized nodes as the reference in localization and using niching idea to cope with localization ambiguity problem resulting from collinear anchors. For the second problem, the algorithm utilizes three criteria to choose a minimum set of reliable neighbouring anchors to localize an unknown node. Three criteria are given to choose reliable neighbouring anchors or localized nodes when localizing an unknown node, including distance, angle, and localization precision. The proposed algorithm has been compared with some existing range-based and distributed algorithms, and the results show that the proposed algorithm achieves higher localization accuracy with less time complexity than the current PSO-based localization algorithms and performs well for wireless sensor networks with coverage holes.

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A hybrid model using fuzzy logic and an extreme learning machine with vector particle swarm optimization for wireless sensor network localization

Cited by 86 publications

References 35 publications

Photosynthetic rate prediction model of newborn leaves verified by core fluorescence parameters

Photosynthetic rate prediction model of newborn leaves verified by core fluorescence parameters

Localization of a Mobile Device with Sensor Using a Cascade Artificial Neural Network-Based Fingerprint Algorithm

Localization of Large‐Scale Wireless Sensor Networks Using Niching Particle Swarm Optimization and Reliable Anchor Selection

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